class_docstr: # Parameters
  
  seed_word_pairs_file : `Union[PathLike, str]`
      Path of file containing seed word pairs.
  tokenizer : `Tokenizer`
      Tokenizer used to tokenize seed words.
  direction_vocab : `Vocabulary`, optional (default=`None`)
      Vocabulary of tokenizer. If `None`, assumes tokenizer is of
      type `PreTrainedTokenizer` and uses tokenizer's `vocab` attribute.
  namespace : `str`, optional (default=`"tokens"`)
      Namespace of direction_vocab to use when tokenizing.
      Disregarded when direction_vocab is `None`.
  requires_grad : `bool`, optional (default=`False`)
      Option to enable gradient calculation for bias direction.
  noise : `float`, optional (default=`1e-10`)
      To avoid numerical instability if embeddings are initialized uniformly.
code: def __init__(
      self,
      seed_word_pairs_file: Union[PathLike, str],
      tokenizer: Tokenizer,
      direction_vocab: Optional[Vocabulary] = None,
      namespace: str = "tokens",
      requires_grad: bool = False,
      noise: float = 1e-10,
  ):
      self.ids1, self.ids2 = load_word_pairs(
          seed_word_pairs_file, tokenizer, direction_vocab, namespace
      )
      self.direction = PairedPCABiasDirection(
          requires_grad=requires_grad
      )
      self.noise = noise
  
imports: import torch
  from typing import Union, Optional
  from os import PathLike
  from allennlp.fairness.bias_direction import BiasDirection, PCABiasDirection, PairedPCABiasDirection, TwoMeansBiasDirection, ClassificationNormalBiasDirection
  from allennlp.fairness.bias_utils import load_word_pairs, load_words
  from allennlp.common import Registrable
  from allennlp.data.tokenizers.tokenizer import Tokenizer
  from allennlp.data import Vocabulary
label_desc: Use image node_id 1 to create a new PairedPCABiasDirectionWrapper object from inherited base classes: BiasDirectionWrapper with example: obj = PairedPCABiasDirectionWrapper(seed_word_pairs_file, tokenizer, direction_vocab, namespace, requires_grad, noise)